EP1290507B1 - Method and system for monitoring and analyzing a paper manufacturing process - Google Patents

Method and system for monitoring and analyzing a paper manufacturing process Download PDF

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Publication number
EP1290507B1
EP1290507B1 EP01925598A EP01925598A EP1290507B1 EP 1290507 B1 EP1290507 B1 EP 1290507B1 EP 01925598 A EP01925598 A EP 01925598A EP 01925598 A EP01925598 A EP 01925598A EP 1290507 B1 EP1290507 B1 EP 1290507B1
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EP
European Patent Office
Prior art keywords
output vector
presented
user
measured
neural network
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Expired - Lifetime
Application number
EP01925598A
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German (de)
English (en)
French (fr)
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EP1290507A2 (en
Inventor
Sakari Laitinen-Vellonen
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Liqum Oy
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Liqum Oy
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Priority claimed from FI20000796A external-priority patent/FI20000796A/fi
Application filed by Liqum Oy filed Critical Liqum Oy
Publication of EP1290507A2 publication Critical patent/EP1290507A2/en
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Publication of EP1290507B1 publication Critical patent/EP1290507B1/en
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
    • G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
    • D—TEXTILES; PAPER
    • D21—PAPER-MAKING; PRODUCTION OF CELLULOSE
    • D21G—CALENDERS; ACCESSORIES FOR PAPER-MAKING MACHINES
    • D21G9/00—Other accessories for paper-making machines
    • D21G9/0009—Paper-making control systems
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0267—Fault communication, e.g. human machine interface [HMI]
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0267—Fault communication, e.g. human machine interface [HMI]
    • G05B23/0272—Presentation of monitored results, e.g. selection of status reports to be displayed; Filtering information to the user

Definitions

  • the invention concerns a method and system for monitoring and analyzing a paper manufacturing process, in which method a large number of quantities are measured from the process and entered as an input vector into a neural network which produces an output vector as a continuous quantity, and in which method at least one fingerprint representing a good process situation, i.e. an optimal output vector is defined, and fingerprints or output vectors obtained in a normal process situation are compared to the said optimal finger-print(s) or output vector(s) substantially in real time and, based on this comparison, the difference is determined and presented to the user in a graphic form.
  • a neural network consists of simple computing elements, neurons, having a plurality of inputs and one response.
  • a neural network is created by linking neurons to each other.
  • the overall operation of the network is a combination of several weighted values which is difficult to understand via individual weights.
  • the weights in the neural network are generally determined by teaching using examples. There are a fairly large number of different network structures and teaching algorithms.
  • Finnish patent application 941058 presents a method for the treatment of especially a paper manufacturing process.
  • a set of measurement results from the process is fed into a neural network, e.g. a perceptron network, and, using a special algorithm, adjustable variables are corrected in order to correct the operating point of the process.
  • the method aims at directly optimizing the quality of the final product, which is determined via 100 - 300 measurements for each machine roll.
  • European patent 815320 discloses a method for process control of a paper machine by utilizing a neural network.
  • a set of measurements comprises spectral values of the substances used, and statements regarding the quality of the product are transmitted via the neural network and corresponding control signals are passed further to a so-called stock preparation stage. In this case, too, product quality is the primary control variable.
  • Spectral (optical) measurements are ill suited for the determination of chemical changes.
  • the object of the present invention is to achieve a new type of method utilizing a neural network in a paper manufacturing process whereby the process can be more easily and accurately analyzed and monitored than before.
  • the basic idea of the invention is only to monitor the process in a reliable manner especially in regard of its runnability.
  • the task of monitoring the quality of the final product is entrusted to other measurement processes because a high quality of a good process situation is generally always achieved when the process situation itself is equally stable.
  • the primary aim of the method of the invention is to describe the runnability of a paper machine. The instant of occurrence of a change in the fingerprints can be seen from history data, so it is possible to find out what changes have taken place in the output variables at this instant.
  • Correcting a bad situation is outside the scope of the present invention, because correcting a given output variable is not a straightforward task as a given difference may be the result of many factors.
  • Local know-how is preferably utilized. The personnel of each plant know their own plant, and this special knowledge is important when the process is to be corrected after it has got into an unstable condition.
  • the output vector of the neural network is processed so as to produce a scalar or otherwise unambiguous quantity.
  • the processing consists of applying a mathematical algorithm, and this can be done using a general-purpose computer.
  • a polar conversion of a continuous difference quantity, expressing the state and history of the process in an extremely concentrated form is presented to the user.
  • both the aforesaid polar conversion and sets of history values are presented side by side to the user, so that, when the process gets into an abnormal state, it will be easier to establish the reason for this.
  • Even incidental causes of process instability can often be established by utilizing local know-how because the precise instant of occurrence of a change can be determined from history data.
  • a multi-layer perceptron neural network is used.
  • variable quantities consisting of various flow, temperature, consistency and pH values and data obtained from preferably electrochemical sensors, are measured from the manufacturing process. These variable quantities form an input vector ( X , t) for a neural network, and an output vector ( Y , t) is generated from them by means of the neural network.
  • an optimal output vector ( Y 0 ,t) has been established. The continuously measured output vector is compared to this optimal output vector, and the difference is produced in a graphic form clearly visible to the user.
  • the geometric distance is computed e.g. as a Euclidean distance.
  • a system according to the invention for use in conjunction with a paper machine 1 can be constructed using general-purpose computers provided with a display device 5 which are provided with an application implementing a neural network 2, computing means 3 and a memory 4.
  • the system is connected to the paper machine 1 partly using existing measuring channels, partly via measuring sensors installed for this purpose.
  • a multi-channel electrochemical sensor 7 is connected to the short circulation of the paper machine.
  • the control 6 and installation of the neural network 2 are preferably implemented using either the same or a separate computer.
  • the broke system, headbox, wire section and press section are measured at least via the process data. Electrochemical measurements in at least some of these provide a substantial addition to the initial data. In the light of latest information, measurement of smell emitted by process liquids also looks promising.
  • the electrochemical measurements include polarization, galvanostatic or only resting potential measurements.
  • the dimension of the output vector of the neural network is generally in the range of 3 - 15; in the examples in Fig. 1 and 2, it is 3 (with 21 input quantities).
  • the minimum dimension is the number of categories to be distinguished.
  • Fig. 2 presents a preferred user interface to the neural network, in which a carefully selected set of data is displayed in a graphic form to the user.
  • an array of bars 11, "Grade profile”, in the upper right-hand part of the display represents a fingerprint consistent with a good process situation, i.e. dimensionless values of the measured quantities presented as bars.
  • displayed on the left is an array of bars 12, "Online profile”, in which the instantaneously measured values are presented as dimensionless values and bars.
  • a third array of bars 14, "Difference profile” showing the difference between each measured value and the corresponding optimal fingerprint value as a corresponding bar.
  • the arrays of bars can naturally be presented as a continuous curve, although in both cases the same information is presented.
  • the display presents a set of history values 13, "Color chart", showing bars which have been generated from the measured quantities (x i ) over a period of time corresponding to the same order as the trend, successive measurement values being displayed as a bar 13' of brightness/color information in which each value (x i ,t n ) is shown as a transverse line of corresponding brightness/color. If the brightness/color of the bar remains unchanged, then its value has not varied, whereas confirmed changes are clearly visible as variations in bar color/brightness.
  • each bar in the set of history values can be presented as a separate curve 15 according to selection 15'.
  • the runnability of the machine is influenced by the electrochemical surface charges which are present on different rollers, wires and felts and which, with time, further have an effect on the accumulation of dirt in the machine
  • the most advantageous alternative will probably be to measure the electrochemical values of the initial materials, especially those of the liquid flows in pulp production and process waters, as inputs to the neural network.
  • the detection of small of the waters will also be advantageous in this connection because certain changes, e.g. microbiological reactions produce a distinct smell trace before they can be discerned in the electrochemistry.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Human Computer Interaction (AREA)
  • Evolutionary Computation (AREA)
  • Mathematical Physics (AREA)
  • Artificial Intelligence (AREA)
  • Paper (AREA)
  • Testing Or Calibration Of Command Recording Devices (AREA)
  • Analysing Materials By The Use Of Radiation (AREA)
  • Collating Specific Patterns (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
EP01925598A 2000-04-05 2001-04-04 Method and system for monitoring and analyzing a paper manufacturing process Expired - Lifetime EP1290507B1 (en)

Applications Claiming Priority (5)

Application Number Priority Date Filing Date Title
FI20000796A FI20000796A (fi) 2000-04-05 2000-04-05 Menetelmä ja järjestelmä paperinvalmistusprosessin monitoroimiseksi ja analysoimiseksi
FI20000796 2000-04-05
US24191900P 2000-10-20 2000-10-20
US241919P 2000-10-20
PCT/FI2001/000324 WO2001075222A2 (en) 2000-04-05 2001-04-04 Method and system for monitoring and analyzing a paper manufacturing process

Publications (2)

Publication Number Publication Date
EP1290507A2 EP1290507A2 (en) 2003-03-12
EP1290507B1 true EP1290507B1 (en) 2004-09-08

Family

ID=26160985

Family Applications (1)

Application Number Title Priority Date Filing Date
EP01925598A Expired - Lifetime EP1290507B1 (en) 2000-04-05 2001-04-04 Method and system for monitoring and analyzing a paper manufacturing process

Country Status (9)

Country Link
US (1) US6792388B2 (es)
EP (1) EP1290507B1 (es)
AT (1) ATE275740T1 (es)
AU (1) AU2001252298A1 (es)
CA (1) CA2404968C (es)
DE (1) DE60105421T2 (es)
ES (1) ES2228845T3 (es)
PT (1) PT1290507E (es)
WO (1) WO2001075222A2 (es)

Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FI20025023A (fi) * 2001-08-21 2003-02-22 Liqum Oy Menetelmä paperi- tai selluprosessissa massa- ja kiertovesijärjestelmän kemiallisen tilan hallitsemiseksi
FI115081B (fi) * 2001-10-19 2005-02-28 Metso Automation Oy Menetelmä ja laitteisto paperikoneen massaosaston toiminnan ohjaamiseksi
WO2004019779A1 (ja) * 2002-08-27 2004-03-11 Dainippon Pharmaceutical Co., Ltd. バイタルサイン表示装置およびその方法
FI20021901A0 (fi) * 2002-10-24 2002-10-24 Liqum Oy Menetelmä prosessin valvomiseksi
DE10305596A1 (de) * 2003-02-11 2004-09-02 Voith Paper Patent Gmbh Verfahren zum Erstellen einer standardisierten Zustandsanalyse von bei der Herstellung einer Faserstoffbahn die Qualität der herzustellenden Faerstoffbahn beeinflussenden Parametern und/oder Betriebszuständen und Maschine zur Durchführung des Verfahrens
US7050875B2 (en) * 2003-07-01 2006-05-23 General Electric Company System and method for detecting an anomalous condition
US20050004684A1 (en) * 2003-07-01 2005-01-06 General Electric Company System and method for adjusting a control model
US6980874B2 (en) * 2003-07-01 2005-12-27 General Electric Company System and method for detecting an anomalous condition in a multi-step process
US6993404B2 (en) * 2003-07-11 2006-01-31 Mks Instruments, Inc. Graphical user interface with process quality indicator
DE102004020495A1 (de) * 2004-04-26 2005-11-24 Siemens Ag Verfahren und Anlage zur Aufbereitung von Altpapier
DE102009011217A1 (de) * 2009-03-04 2010-09-09 Siemens Aktiengesellschaft Steuerung der erweiterten Trockenpartie einer Papiermaschine
EP3392405A1 (de) * 2017-04-18 2018-10-24 Siemens Aktiengesellschaft Verfahren zum betrieb einer papiermaschine, antriebssystem und papiermaschine

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1992014197A1 (en) 1991-02-08 1992-08-20 Kabushiki Kaisha Toshiba Model forecasting controller
US5671335A (en) * 1991-05-23 1997-09-23 Allen-Bradley Company, Inc. Process optimization using a neural network
DE19510009C2 (de) * 1995-03-23 2000-05-11 Siemens Ag Verfahren und Vorrichtung zur Prozeßführung einer Papiermaschine
DE59712546D1 (de) * 1997-07-31 2006-04-06 Sulzer Markets & Technology Ag Verfahren zum Überwachen von Anlagen mit mechanischen Komponenten
US5960381A (en) 1998-07-07 1999-09-28 Johnson Controls Technology Company Starfield display of control system diagnostic information

Also Published As

Publication number Publication date
WO2001075222A2 (en) 2001-10-11
CA2404968C (en) 2011-08-30
AU2001252298A1 (en) 2001-10-15
PT1290507E (pt) 2005-01-31
US20030139904A1 (en) 2003-07-24
DE60105421T2 (de) 2005-09-22
DE60105421D1 (de) 2004-10-14
CA2404968A1 (en) 2001-10-11
WO2001075222A3 (en) 2001-12-13
EP1290507A2 (en) 2003-03-12
US6792388B2 (en) 2004-09-14
ATE275740T1 (de) 2004-09-15
ES2228845T3 (es) 2005-04-16

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